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hive-mind

Coordinate multiple AI agents through queen-led hierarchy and Byzantine consensus.

1|Updated Dec 22, 2017
Search Tags:#ai agents#memory#consensus#collective intelligence#multi-agent systems#coordination
One-click install
npx skills add https://github.com/coreyhulen/enviroment --skill hive-mind
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hive-mind
Source: https://github.com/coreyhulen/enviroment/tree/main/claude-init/skills/hive-mind
Command: npx skills add https://github.com/coreyhulen/enviroment --skill hive-mind

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of coordinating multiple AI agents to achieve complex objectives by providing a robust framework for hierarchical control, consensus-driven decision-making, and shared persistent memory.

Core Features & Use Cases

  • Queen-Led Architecture: Orchestrate agents through strategic, tactical, and adaptive queens.
  • Specialized Workers: Deploy diverse agents like researchers, coders, and testers.
  • Collective Memory: Utilize a shared, persistent knowledge base with caching and consolidation.
  • Consensus Mechanisms: Ensure reliable decision-making with majority, weighted, or Byzantine fault-tolerant voting.
  • Use Case: Coordinate a team of AI agents to autonomously develop a new software feature, from initial research and design to coding, testing, and documentation, ensuring all agents work cohesively towards the common goal.

Quick Start

Spawn a swarm of agents to build a microservices architecture.

Frequently Asked Questions about hive-mind

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I coordinate multiple AI agents to build a software feature autonomously?▼

You coordinate multiple AI agents by deploying a hierarchical architecture with strategic and tactical queens that oversee specialized worker agents like researchers, coders, and testers to execute complex software projects cohesively.

What is Byzantine consensus in multi-agent systems?▼

Byzantine consensus in multi-agent systems is a fault-tolerant voting mechanism that ensures reliable decision-making and coordination among distributed AI agents even when some agents fail or act unpredictably.

Can I use a collective memory system to share persistent knowledge across AI agents?▼

Yes, you can use a collective memory system to provide a shared, persistent knowledge base across AI agents, enabling caching and consolidation of information for continuous project execution.

What's the best way to manage autonomous decision-making for specialized worker agents?▼

The best way to manage autonomous decision-making for specialized worker agents is through a queen-led hierarchy that utilizes majority, weighted, or Byzantine consensus mechanisms for reliable coordination.

Does multi-agent coordination work for complex project execution like microservices architecture?▼

Yes, multi-agent coordination works for complex project execution such as building microservices architectures by spawning swarms of specialized agents for research, design, coding, and testing.

Why use a queen-led hierarchical architecture for multi-agent AI coordination?▼

You use a queen-led hierarchical architecture to orchestrate AI agents through strategic, tactical, and adaptive layers, enabling sophisticated control, consensus-driven decisions, and specialized task delegation.

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